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2008 International Conference on BioMedical Engineering and Informatics
Classification of Surface EMG Signal Based on Energy Spectra Change
May 27-May 30
ISBN: 978-0-7695-3118-2
ASCII Text
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Xiao Hu, Ping Yu, Qun Yu, Waixi Liu, Jian Qin, "Classification of Surface EMG Signal Based on Energy Spectra Change," BioMedical Engineering and Informatics, International Conference on, vol. 2, pp. 375-379, 2008 International Conference on BioMedical Engineering and Informatics, 2008.
 
 
BibTex
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@article{ 10.1109/BMEI.2008.24,
author = {Xiao Hu and Ping Yu and Qun Yu and Waixi Liu and Jian Qin},
title = {Classification of Surface EMG Signal Based on Energy Spectra Change},
journal ={BioMedical Engineering and Informatics, International Conference on},
volume = {2},
year = {2008},
isbn = {978-0-7695-3118-2},
pages = {375-379},
doi = {http://doi.ieeecomputersociety.org/10.1109/BMEI.2008.24},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - BioMedical Engineering and Informatics, International Conference on
TI - Classification of Surface EMG Signal Based on Energy Spectra Change
SN - 978-0-7695-3118-2
SP375
EP379
A1 - Xiao Hu,
A1 - Ping Yu,
A1 - Qun Yu,
A1 - Waixi Liu,
A1 - Jian Qin,
PY - 2008
KW - surface EMG signal
KW - wavelet transform
KW - energy spectra
KW - pattern recognition
VL - 2
JA - BioMedical Engineering and Informatics, International Conference on
ER -
 
In this paper, we introduced a novel and simple methods of extracting the general features of two surface EMG signal patterns: forearm supination (FS) and forearm pronation (FP) surface EMG signals. The surface EMG signal was divided into two segments appropriately at the preparation stage and the action stages. Relative wavelet packet energy (RWPE, symbolized by pnp and pna respectively at the preparation stage and the action stages, where n denotes the nth frequency band (FB)) of the surface EMG signal was firstly calculated, and then the change (Pn=pna-pnp) was determined. The results show that Pn from some FBs could effectively characterize the general characteristics of the two surface EMG signal patterns. Compared with Pn in other FBs, P4 (in 93.75-125 Hz) had more appropriate features.
Index Terms:
surface EMG signal, wavelet transform, energy spectra, pattern recognition
Citation:
Xiao Hu, Ping Yu, Qun Yu, Waixi Liu, Jian Qin, "Classification of Surface EMG Signal Based on Energy Spectra Change," bmei, vol. 2, pp.375-379, 2008 International Conference on BioMedical Engineering and Informatics, 2008
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